Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Rank-One Approximation to High Order Tensors
SIAM Journal on Matrix Analysis and Applications
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
A Reflective Symmetry Descriptor for 3D Models
Algorithmica
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Monocular Human Motion Capture with a Mixture of Regressors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Pedestrian Tracking Based on Colour and Spatial Information
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Towards Multi-View Object Class Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Non-isometric manifold learning: analysis and an algorithm
Proceedings of the 24th international conference on Machine learning
Regression on manifolds using kernel dimension reduction
Proceedings of the 24th international conference on Machine learning
Transductive regression piloted by inter-manifold relations
Proceedings of the 24th international conference on Machine learning
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
Reactive grasping using optical proximity sensors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning to place new objects in a scene
International Journal of Robotics Research
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We propose a learning algorithm for estimating the 3-D orientation of objects. Orientation learning is a difficult problem because the space of orientations is non-Euclidean, and in some cases (such as quaternions) the representation is ambiguous, in that multiple representations exist for the same physical orientation. Learning is further complicated by the fact that most man-made objects exhibit symmetry, so that there are multiple "correct" orientations. In this paper, we propose a new representation for orientations--and a class of learning and inference algorithms using this representation-- that allows us to learn orientations for symmetric or asymmetric objects as a function of a single image. We extensively evaluate our algorithm for learning orientations of objects from six categories.